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Deep Learning Fusion for Multimedia Malware Classification

Deep Learning Fusion for Multimedia Malware Classification

Yassine Maleh
Copyright: © 2023 |Pages: 28
ISBN13: 9781668472163|ISBN10: 1668472163|ISBN13 Softcover: 9781668472170|EISBN13: 9781668472187
DOI: 10.4018/978-1-6684-7216-3.ch003
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MLA

Maleh, Yassine. "Deep Learning Fusion for Multimedia Malware Classification." Recent Advancements in Multimedia Data Processing and Security: Issues, Challenges, and Techniques, edited by Ahmed A. Abd El-Latif, et al., IGI Global, 2023, pp. 46-73. https://doi.org/10.4018/978-1-6684-7216-3.ch003

APA

Maleh, Y. (2023). Deep Learning Fusion for Multimedia Malware Classification. In A. Abd El-Latif, M. Ahmad Wani, Y. Maleh, & M. El-Affendi (Eds.), Recent Advancements in Multimedia Data Processing and Security: Issues, Challenges, and Techniques (pp. 46-73). IGI Global. https://doi.org/10.4018/978-1-6684-7216-3.ch003

Chicago

Maleh, Yassine. "Deep Learning Fusion for Multimedia Malware Classification." In Recent Advancements in Multimedia Data Processing and Security: Issues, Challenges, and Techniques, edited by Ahmed A. Abd El-Latif, et al., 46-73. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7216-3.ch003

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Abstract

In the face of escalating cyber threats posed by malware, advanced detection techniques are crucial. This study introduces a cutting-edge approach that merges convolutional neural networks (CNNs) and long short-term memory recurrent neural networks (LSTMs) for enhanced malware classification. The effectiveness of this method is rigorously examined using Microsoft's BIG Cup 2015 dataset. By combining CNN's ability to capture local features and LSTM's proficiency in processing sequence data, our approach achieves remarkable accuracy (98.73%) in identifying malicious behaviors. This research contributes an extensive exploration of deep learning models, an innovative CNN-LSTM hybrid architecture, and a comprehensive case study showcasing its superior performance. The presented approach signifies a significant stride in bolstering cybersecurity against the ever-evolving threat of malware.

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